Cellular heterogeneity and cytokine signatures in acute myeloid leukemia: A novel prognostic model.

Abstract

Acute Myeloid Leukemia (AML) is a complex hematological malignancy distinguished by its heterogeneity in genetic aberrations, cellular composition, and clinical outcomes. This diversity complicates the development of effective, universally applicable therapeutic strategies and highlights the necessity for personalized approaches to treatment. In our study, we utilized high-resolution single-cell RNA sequencing from publicly available datasets to dissect the complex cellular landscape of AML. This approach uncovered a diverse array of cellular subpopulations within the bone marrow samples of AML patients. Through meticulous analysis, we identified 156 differentially expressed cytokine-related genes that underscore the nuanced interplay between AML cells and their microenvironment. Leveraging this comprehensive dataset, we constructed a prognostic risk score model based on seven pivotal cytokine-related genes: CCL23, IL2RA, IL3RA, IL6R, INHBA, TNFSF15, and TNFSF18. The mRNA levels of 7 genes in the risk score model have significant different. This model was rigorously validated across several independent AML patient cohorts, showcasing its robust prognostic capability to stratify patients into distinct risk categories. Patients classified under the high-risk category exhibited significantly poorer survival outcomes compared to their low-risk counterparts, underscoring the model's clinical relevance. Additionally, our in-depth investigation into the immune landscape revealed marked differences in immune cell infiltration and cytokine signaling between the identified risk groups, shedding light on potential immune-mediated mechanisms driving disease progression and treatment resistance. This comprehensive analysis not only advances our understanding of the cellular and molecular underpinnings of AML but also introduces a novel, clinically applicable risk score model. This tool holds significant promise for enhancing the precision of prognostic assessments in AML, thereby paving the way for more tailored and effective therapeutic interventions. Our findings represent a pivotal step toward the realization of personalized medicine in the management of AML, offering new avenues for research and treatment optimization in this challenging disease landscape.